114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.svm import SVC
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from sklearn.linear_model import SGDClassifier, LinearRegression
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from sklearn.metrics import confusion_matrix, classification_report, r2_score, accuracy_score
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from sklearn.neural_network import BernoulliRBM, MLPClassifier, MLPRegressor
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from sklearn.preprocessing import StandardScaler, LabelEncoder
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from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
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class WinePredict:
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def __init__(self):
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self.wine = pd.read_csv('./wine.csv', sep=';')
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X = self.wine.drop('quality', axis=1)
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y = self.wine['quality']
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self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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# 特征归一化
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sc = StandardScaler()
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self.X_train = sc.fit_transform(self.X_train)
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self.X_test = sc.fit_transform(self.X_test)
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def lr(self):
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"""
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线性回归
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"""
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lr = LinearRegression()
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lr.fit(self.X_train, self.y_train)
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return lr.predict(self.X_test)
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def rfc(self):
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"""
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随机森林
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"""
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rfc = RandomForestClassifier(n_estimators=200, random_state=20)
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rfc.fit(self.X_train, self.y_train)
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return rfc.predict(self.X_test)
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def sgd(self):
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"""
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随机梯度下降
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"""
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sgd = SGDClassifier(penalty=None)
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sgd.fit(self.X_train, self.y_train)
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return sgd.predict(self.X_test)
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def svc(self):
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"""
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支持向量机
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"""
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svc = SVC(C=1.4, gamma=0.8, kernel='rbf')
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svc.fit(self.X_train, self.y_train)
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return svc.predict(self.X_test)
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def mlp(self):
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"""
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多层感知器
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"""
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mlp = MLPClassifier([10, 6], learning_rate_init=0.001, activation='relu', solver='adam', alpha=0.0001,
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max_iter=30000)
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# 神经网络
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mlp.fit(self.X_train, self.y_train)
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return mlp.predict(self.X_test)
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# 参数调优
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def grid_search(self, model, param):
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print('search %s', param)
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grid = GridSearchCV(model, param_grid=param, scoring='accuracy', cv=10)
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grid.fit(self.X_train, self.y_train)
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print("%s: %f" % (grid.best_params_, grid.best_score_))
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def gs_svc(self):
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param = {
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'C': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4],
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'kernel': ['linear', 'rbf'],
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'gamma': [0.1, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4]
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}
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self.grid_search(SVC(), param)
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# {'C': 1.4, 'gamma': 0.8, 'kernel': 'rbf'}
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def gs_rfc(self):
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self.grid_search(RandomForestClassifier(), {
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'n_estimators': [200],
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'random_state': [_ for _ in range(0, 200, 10)]
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})
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def report(self, fc):
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r = fc()
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if r.dtype == 'float64' or r.dtype == 'float32':
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r = r.round()
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# print(classification_report(self.y_test, r))
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print(fc.__doc__.strip())
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print(" R2: %f" % r2_score(self.y_test, r))
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print(" accuracy: %f" % accuracy_score(self.y_test, r))
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def showXY(self):
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# fig = plt.figure(figsize=(10, 6))
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for i in range(len(self.wine.columns[:-1])):
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sns.barplot(x='quality', y=self.wine.columns[i], data=self.wine, ax=plt.subplot(4, 4, i + 1))
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plt.xlabel(self.wine.columns[i])
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plt.ylabel('')
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plt.tight_layout()
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plt.show()
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if __name__ == '__main__':
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wp = WinePredict()
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wp.gs_rfc()
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for i in [wp.rfc, wp.lr, wp.svc, wp.sgd, wp.mlp][:1]:
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wp.report(i)
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# wp.showXY()
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